While there are various devices designed for chemical analysis, one of the more widely used systems involves a physical separation using a chromatograph followed by a mass spectrometer. Various types of mass spectrometers are known which use a mass analyzer and incorporate a time-to-digital converter also known as an ion arrival counter. Time-to-digital converters are used, for example, in time of flight mass analyzers where packets of ions are ejected into a field-free drift region with essentially the same kinetic energy. In the drift region, ions with different mass-to-charge ratios in each packet of ions travel with different velocities and therefore arrive at an ion detector disposed at the exit of the drift region at different times. Measurement of the ion transit-time therefore determines the mass-to-charge ratio of that particular ion.
Currently, one of the more commonly employed ion detectors in time of flight mass spectrometers is a single ion counting detector in which an ion impacting a detecting surface produces a pulse of electrons by means of, for example, an electron multiplier. The pulse of electrons is typically amplified by an amplifier and a resultant electrical signal is produced. The electrical signal produced by the amplifier is used to determine the transit time of the ion striking the detector by means of a time to digital converter which is started once a packet of ions is first accelerated into the drift region. The ion detector and associated circuitry is therefore able to detect a single ion impacting onto the detector.
While many types of mass spectrometers can be used in analyzing compounds, all of these devices produce an extensive data matrix representing the mass spectra that have been measured using the mass spectrometer. These large data matrices can then be analyzed to determine which types of compounds are represented in a particular data matrix output.
The process of reducing a large set of continuously evolving spectra into individual constituent spectra has been addressed using various techniques. Some are based on good laboratory principles, others follow machine learning pathways.
Much has been written and many algorithms have been developed to tackle this problem of converting the spectral output by mass spectrometers into identifiable compounds. The most widely accepted of these has been offered as a complete program called AMDIS. This program is freely available from http://chemdata.nist.gov/mass-spc/amdis/overview.html. AMDIS is based on the automation of good laboratory techniques and the matching of patterns against a large library of compound patterns. However AMDIS is very compute intensive and relatively time consuming. Other algorithms approach the problem using machine learning which has similar drawbacks.